Asta-Advances in Statistical Analysis最新文献

This short paper explores the estimation of a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model. The log-volatility term in this model can depend on (i) the spatial lag of the log-squared outcome variable, (ii) the time-lag of the log-squared outcome variable, (iii) the spatiotemporal lag of the log-squared outcome variable, (iv) exogenous variables, and (v) the unobserved heterogeneity across regions and time, i.e., the regional and time fixed effects. We examine the small- and large-sample properties of two quasi-maximum likelihood estimators and a generalised method of moments estimator for this model. We first summarize the theoretical properties of these estimators and then compare their finite sample properties through Monte Carlo simulations.

Clustering categorical data presents unique challenges that traditional techniques do not adequately address. This paper proposes an extension of the fuzzy C-modes algorithm. By incorporating a noise cluster and integrating spatial contiguity relationships among units, the algorithm’s robustness is significantly enhanced. Performance evaluations using synthetic data demonstrate the efficacy of the proposed algorithm in handling both global and local outliers. Furthermore, the paper discusses the application of the algorithm to real-world data on sustainable urban mobility in the Italian provincial capitals during 2021, highlighting its practical relevance and potential impact in real-world scenarios.

This paper proposes a new statistical approach for assessing treatment effect using Bayesian Networks (BNs). The goal is to draw causal inferences from observational data with a binary outcome and discrete covariates. The BNs are here used to estimate the propensity score, which enables flexible modeling and ensures maximum likelihood properties. When the propensity score is estimated by BNs, two point estimators are considered—Hájek and Horvitz–Thompson—based on inverse probability weighting, and their main distributional properties are derived for constructing confidence intervals and testing hypotheses about the absence of the treatment effect. Empirical evidence is presented to show the good behavior of the proposed methodology through a simulation study mimicking the characteristics of a real dataset of prostate cancer patients from Milan San Raffaele Hospital.

Performance measurement is of paramount importance in the context of sports analytics. A great variety of data analysis methods has been exploited to this aim. All these proposals almost never include resorting to survival analysis techniques, although time-to-event data are suitable for addressing this issue. This work aims to identify the main achievements of a National Basketball Association player that affect the time it takes for him to exceed a given threshold of points. In order to identify nonlinear effects and possible interactions among the predictors, the analysis is carried out with machine learning methods, specifically survival trees and random survival forests.

We discuss the estimation and forecast of long-memory models for count data time series. We first demonstrate by Monte Carlo simulations that the Whittle estimator is the most appropriate for recovering the memory degree of a count data time series. In the following, we introduce the possibility of forecasting count data by exploiting the infinite autoregressive representation of the model. We complete our analysis with an empirical example in which we verify the predictability of the price jump numbers.

Wearable devices and sensors have recently become a popular way to collect data, especially in the health sciences. The use of sensors allows patients to be monitored over a period of time with a high observation frequency. Due to the continuous-on-time structure of the data, novel statistical methods are recommended for the analysis of sensor data. One of the popular approaches in the analysis of wearable sensor data is functional data analysis. The main objective of this paper is to review functional data analysis methods applied to wearable device data according to the type of sensor. In addition, we introduce several freely available software packages and open databases of wearable device data to facilitate access to sensor data in different fields.

This paper aims to introduce a novel approach to spatial blind source separation (SBSS) that addresses the limitations of existing methods. Current SBSS techniques rely on the joint diagonalization of multiple local covariance functions, all of which assume isotropy. To overcome this constraint, anisotropic local covariance matrices that relax the isotropy assumption are proposed. A simulation study and an application on real-world data demonstrate the performance improvement obtained by incorporating these anisotropic covariance matrices into the SBSS framework and highlight the potential of this new approach for more accurate and flexible source separation in spatial data analysis.